利用SNL-EFDC验证海洋水动能生成模拟

S. James, S. Lefantzi, Janet Barco, Erick Johnson, Jesse D. Roberts
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引用次数: 20

摘要

人们对海洋水动力(MHK)能源的兴趣日益浓厚,这导致了对新兴技术的最佳配置进行了大量研究,以最大限度地获取能源并最大限度地减少对海洋环境的影响。了解近场和远场流体力学的变化对于评估最佳安置是必要的。MHK项目将从系统中转换能量(动量),改变水流速度,并可能改变水质和沉积物的输送。水电项目的最大场地效率必须与避免环境危害的要求相平衡。本研究基于先前对现有水流、泥沙动力学和水质规范(SNL-EFDC)的修改,其中使用了一个实验水槽的模拟来确定、量化和可视化MHK能量产生的影响。湍流和设备参数是根据南安普顿大学水槽实验的尾流数据进行校准的(L. Myers和a . S. Bahaj,“水平轴海流涡轮机的近尾流特性”,发表于第八届欧洲波浪和潮汐能源会议论文集,2009年,第558-565页),以产生mhk设备能量去除的验证模拟。为了在设备尾迹内实现真实的速度赤字,使用非线性、模型无关的参数估计器PEST和DAKOTA进行参数研究,以确定流动和湍流闭合方程中各种常数的参数灵敏度和最佳值。灵敏度分析表明,Smagorinski亚网格尺度水平动量扩散常数和k-ε动能耗散率常数(Cε4)是影响尾迹分布和下游10个及以上装置直径处耗散的两个最重要参数,它们强烈影响尾迹与体流的混合情况。这些结果验证了该模型,该模型现在可以用于进行mhk阵列分布和优化研究。
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Verifying marine-hydro-kinetic energy generation simulations using SNL-EFDC
Increasing interest in marine hydrokinetic (MHK) energy has led to significant research regarding optimal placement of emerging technologies to maximize energy capture and minimize effects on the marine environment. Understanding the changes to the near- and far-field hydrodynamics is necessary to assess optimal placement. MHK projects will convert energy (momentum) from the system, altering water velocities and potentially water quality and sediment transport as well. Maximum site efficiency for MHK power projects must balance with the requirement of avoiding environmental harm. This study is based on previous modification to an existing flow, sediment dynamics, and water-quality code (SNL-EFDC) where a simulation of an experimental flume is used to qualify, quantify, and visualize the influence of MHK energy generation. Turbulence and device parameters are calibrated against wake data from a flume experiment out of the University of Southampton (L. Myers and A. S. Bahaj, “Near wake properties of horizontal axis marine current turbines,” in Proceedings of the 8th European Wave and Tidal Energy Conference, 2009, pp. 558–565) to produce verified simulations of MHK-device energy removal. To achieve a realistic velocity deficit within the wake of the device, parametric studies using the nonlinear, model-independent, parameter estimators PEST and DAKOTA were compared to determine parameter sensitivities and optimal values for various constants in the flow and turbulence closure equations. The sensitivity analyses revealed that the Smagorinski subgrid-scale horizontal momentum diffusion constant and the k-ε kinetic energy dissipation rate constant (Cε4) were the two most important parameters influencing wake profile and dissipation at 10 or more device diameters downstream as they strongly influence how the wake mixes with the bulk flow. These results verify the model, which can now be used to perform MHK-array distribution and optimization studies.
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